A Reexamination of Real Earnings Management from a Firm-Specific Time-Series

Perspective

E. SCOTT JOHNSON

Virginia Tech University

T. TAYLOR JOO

New Mexico State University

MICHAEL D. STUART

Vanderbilt University

May 2015

Early draft. Please do not cite.

Keywords: Real earnings management, Earnings management, Earnings manipulation, Discretionary expenses,

Firm-specific forecasts, Managerial ability.

Data Availability: Data are publicly available from sources identified in the text.

We thank Brooke Beyer, Bowe Hansen, Jing Huang, James Myers, Mitch Oler, Velina Popova, Sarah Stein, Michael

Wolfe, and participants at the 2014 Arkansas Research Conference for helpful comments and suggestions. We also

thank Peter Demerjian for generously providing access to MA-Score data.

A Reexamination of Real Earnings Management from a Firm-Specific Time-Series

Perspective

ABSTRACT: Managers use real earnings management (REM) to influence reported earnings

through the manipulation of real business activities. Extant literature generally employs cross-

sectional analysis to identify REM, but newer studies suggest that these traditional REM

measures are severely mis-specified. In this study, we employ a new methodology that utilizes

firm-specific time-series characteristics of discretionary expenses to identify REM. Using this

new REM measure, we find that firms engage in REM to meet or just beat zero earnings and to

meet or just beat consensus analyst forecasts, a finding that is not strongly supported by

traditional REM measures. Prior literature also suggests that some managers and researchers

believe that REM is myopic and imposes real costs on firms, but there is mixed empirical

evidence regarding the impact of REM on future operating performance. We find that firms that

opportunistically engage in REM perform, on average, neither better nor worse in the future, a

finding consistent with survey responses suggesting that managers are careful not to sacrifice

future operating performance in order to meet or just beat earnings benchmarks. Additional

analysis shows that firms with high ability managers that engage in REM have better future

operating performance.

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1. Introduction

Prior literature suggests that managers engage in earnings management in order to report

earnings that meet or just beat certain benchmarks. Earnings management is generally classified

as either accruals management (AM) or real earnings management (REM).1 AM refers to the use

of discretionary accruals, either within or outside of generally accepted accounting principles

(GAAP), to opportunistically influence earnings. Common examples of AM include

underestimating bad debt and accelerating the recognition of sales. REM refers to managerial

decisions that influence earnings through real business activities. Roychowdhury (2006, 337)

defines REM as “…departures from normal operational practices, motivated by managers’ desire

to mislead at least some stakeholders into believing certain financial reporting goals have been

met in the normal course of operations.” Examples of REM include opportunistically cutting

discretionary expenses such as advertising, research and development (R&D), and selling,

general and administrative (SG&A). Extant literature generally employs cross-sectional analysis

to identify firms engaging in REM (e.g., Roychowdhury 2006; Cohen, Dey and Lys 2008; Cohen

and Zarowin 2010; McInnis and Collins 2011; Zang 2012; Zhao, Chen, Zhang and Davis 2012),

but newer studies suggest that traditional REM measures are severely mis-specified (Cohen,

Pandit, Wasley and Zach 2014; Siriviriyakul 2014). Additionally, empirical evidence on the

impact of REM on future operating performance is mixed. In this study, we employ a new

methodology to identify REM that utilizes firm-specific time-series characteristics of

discretionary expenses. We then examine the effect of REM on future operating performance.

Emerging research questions the validity of the REM proxies used in the majority of

extant studies. Cohen et al. (2014) find that traditional REM measures used in the literature are

1 Throughout this study, and consistent with prior literature, we use the terms “real earnings management,” “real

activities management,” and “real activities manipulation” interchangeably.

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“severely mis-specified.” Similarly, analysis of current REM models by Siriviriyakul (2014)

suggests the presence of omitted variables. This mis-specification raises the possibility that the

findings of prior research are not robust to alternate specifications. Prior research utilizes cross-

sectional analysis to estimate abnormal discretionary expenses and identify firms engaging in

real earnings management. In practice, however, financial statement analysis relies on firms’

individual characteristics (Stickney, Brown and Wahlen 2003; White, Sondhi and Fried 2003;

Lundholm and Sloan 2009; Penman 2013). Additionally, Francis and Smith (2005) argue that

earnings analysis should be performed using firm-specific time-series estimations because the

persistence of a firm’s earnings components are unlikely to depend on the persistence of other

firms’ similar components.

Anecdotal evidence suggests that managers engage in REM to meet certain benchmarks.

Based on survey results, Graham, Harvey and Rajgopal (2005) find that 80% of chief financial

officers (CFOs) would decrease discretionary spending in order to meet an earnings benchmark

and 55.3% would delay starting a new project to meet an earnings benchmark, even if the delay

entailed a small sacrifice in value. However, respondents indicate that they would take real

actions to boost earnings only if the real sacrifices are not too large. This suggests that managers

understand the potential costs associated with REM, but it also suggests that they attempt to

mitigate those costs even while engaging in REM to meet earnings benchmarks.

Theoretical research suggests that REM imposes value-destroying costs on firms. Ewert

and Wagenhofer (2005, 1102) state that real earnings management imposes real costs on the

firm. Additionally, Jensen (2005, 8) states that “…when numbers are manipulated to tell the

markets what they want to hear (or what managers want them to hear) rather than the true state of

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the firm – it is lying, and when real operating decisions that would maximize value are

compromised to meet market expectations, real long-term value is being destroyed.”

However, prior empirical research provides mixed evidence on the impact of REM on

future performance. On the one hand, some studies find that REM imposes value-destroying

costs. Cohen and Zarowin (2010) find that seasoned equity offering (SEO) firms often engage in

both forms of earnings management (i.e., REM and AM), and of the firms that suffer post-SEO

declines in performance, the declines are more severe for firms engaging in greater levels of

REM than for firms engaging in greater levels of AM. Additionally, Francis, Hasan and Li

(2011b) find that firms engaging in REM are more likely to suffer subsequent stock price crashes

than firms not engaging in REM, and Zhao et al. (2012) find that abnormally low discretionary

expenses and production costs lead to lower future performance in the absence of just meeting

earnings benchmarks. On the other hand, some studies do not find a negative impact from REM

on future performance. For instance, Taylor and Xu (2010) find that REM does not lead to a

decline in subsequent operating performance, and Gunny (2010) provides evidence that firms

that engage in REM to meet certain earnings benchmarks have better future operating

performance than firms that either miss the benchmarks or that meet the benchmarks without

engaging in REM.

This study is dually motivated by (1) the need to develop an alternate method for

identifying REM, given the growing evidence that traditional REM models are mis-specified,

and (2) the mixed empirical evidence regarding the impact of REM on future operating

performance.

To conduct our analyses, we construct a preliminary sample of 31,782 firm-year

observations (representing 4,383 unique firms) between 1995 and 2013. We first employ a firm-

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specific time-series methodology to estimate expected discretionary expenses (i.e., the sum of

advertising, R&D, and SG&A costs). We then calculate the abnormal level of discretionary

expenses as the difference between actual and expected discretionary expenses. Because prior

research suggests that traditional REM measures are severely mis-specified, we examine the

properties of our firm-specific time-series measure of abnormal discretionary expenses (Firm-

Specific REM) and compare these properties to the traditional REM measure (Traditional REM).

Siriviriyakul (2014) states that the Traditional REM measure is highly persistent, which we

confirm through an examination of the sign (positive or negative) of the abnormal discretionary

expenses over time. The Firm-Specific REM measure proves to be much less persistent and

exhibits the variation we would expect to see if managers are making myopic decisions about

discretionary expenses in order to meet certain earnings benchmarks.

Next, we examine whether Firm-Specific REM is associated with the likelihood of

meeting certain benchmarks. Because we compare our measure to the Traditional REM measure

first developed by Roychowdhury (2006), we use the same benchmarks utilized in his study:

zero earnings and the consensus analyst forecast.2 We first use the Firm-Specific REM measure

and find that it is negatively associated with the likelihood of meeting or just beating both zero

earnings and the consensus analyst forecast, at the 5% and 1% level, respectively.3 This result

suggests that the Firm-Specific REM measure is identifying manipulations in real activities to

meet these important benchmarks. Next, we use the Traditional REM measure and find that it is

negatively associated with the likelihood of meeting or just beating zero earnings, but only at the

2 It is important to note that although Roychowdhury (2006) does test for REM to meet or just beat the consensus

analyst forecast, he does not use the same measure (Traditional REM) that he utilizes to test for REM to avoid

losses. Instead, he uses an alternate specification that is based on a performance-matching technique advocated by

Kothari, Leone, and Wasley (2005). 3 Note that lower levels of abnormal discretionary expenses result in higher net income.

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10% level. We do not, however, find an association between Traditional REM and meeting or

just beating the consensus analyst forecasts. Overall, these results suggest that the Firm-Specific

REM measure is more accurately identifying REM than the Traditional REM measure.

In supplemental analyses, we classify firms with negative abnormal discretionary

expenses in year t but positive abnormal discretionary expenses in year t-1 and t+1 as “blip”

suspect firms and examine whether these firms engage in REM to meet or just beat zero earnings

or prior year’s earnings.4 A common criticism of REM research is that negative abnormal

discretionary expenses are simply permanent cost cutting decisions made by management to

improve future profitability or are part of the normal operations of the firm. In other words, it

may not be real earnings management at all, but rather “good’ management, a change in

management philosophy, or inherent firm differences. By identifying firms that have positive

abnormal discretionary expenses in year t-1, followed by negative abnormal discretionary

expenses in year t, and then returning to positive abnormal discretionary expenses in year t+1,

we attempt to decrease the likelihood that our suspect firms are simply cutting costs in an effort

to improve future profitability. We utilize this “blip” in discretionary expenses to identify firms

that strategically manipulate real activities in year t to meet certain benchmarks. As predicted, we

find a positive and statistically significant association, at the 1% level, between “blip” firms,

determined by utilizing the Firm-Specific REM measure, and the likelihood of meeting or just

beating zero earnings or last year’s earnings. However, we do not find a statistically significant

association between “blip” firms, determined using the Traditional REM measure, and the same

benchmarks. This test provides additional evidence of the validity of the Firm-Specific REM

4 Change in earnings is an important benchmark in earnings management literature that is addressed, but not tested,

in Roychowdhury (2006). We add this benchmark to our tests as a robustness check for the validity of our measure

of abnormal discretionary expenses, as we believe that managers may engage in REM to meet or just beat last year’s

earnings.

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measure and introduces a new way of identifying REM firms through outwardly-visible

indicators.

We also replicate Gunny (2010) to examine the impact of REM on future performance. In

contrast to the findings in Gunny (2010), we do not find evidence that firms that engage in REM

to report small positive earnings perform better in the future using the new Firm-Specific REM

measure. However, we also do not find evidence that firms that engage in REM to report small

positive earnings perform worse in the future. These results suggest that, on average, engaging in

REM to meet earnings benchmarks has no discernible impact on future operating performance.

This may be due to the fact that, while managers engage in REM to meet earnings benchmarks,

they are careful not to sacrifice future operating performance. This finding is consistent with the

Graham et al. (2005) survey findings that managers weigh the costs and benefits of engaging in

REM. In additional analyses, we find some evidence that firms with high ability managers that

engage in REM perform better in the future, suggesting that either high ability managers are

better at mitigating the negative impact of REM on future performance or that behavior that

appears to be REM, when engaged in by firms with high ability managers, is actually just “good”

management.

Although there are other studies examining the consequences of REM, there is no

consensus on its effect on future performance, and given recent evidence suggesting that

traditional REM models are severely mis-specified, we believe a re-examination of the topic is

warranted. This study contributes to the REM literature by (1) developing a new time-series

firm-specific empirical method to identify income-increasing REM, and (2) reexamining the

impact of REM on future performance using this new methodology.

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Our results should be of interest to investors, because they reveal that, contrary to prior

research (i.e., Gunny 2010), engaging in REM to meet earnings benchmarks does not appear to

lead to better future operating performance. Additionally, we find that REM firms do perform

better in the future when led by high ability managers. Finally, our results should be of interest to

accounting researchers as they consider the measurement of real earnings management and

evaluate the effect of this behavior on future performance.

Our paper proceeds as follows. Section 2 reviews prior evidence from the REM literature

and develops our hypotheses. Section 3 describes our sample and research design. Section 4

presents our empirical results. Section 5 concludes.

2. Background and Hypothesis Development

Anecdotal evidence suggests that managers engage in real activities manipulation to

influence earnings. Graham et al. (2005) provide evidence, from surveys, that managers are

willing to manipulate real activities, even if the manipulations have the potential to reduce firm

value, as long as the negative impact is only a “…small sacrifice in value.” In fact, Graham et al.

(2005, 29) indicate that several CFOs argue, “…you have to start with the premise that every

company manages earnings.” Of particular importance to our study, Graham et al. (2005) report

that a larger number of managers admit to reducing discretionary expenses than to engaging in

other methods of real activities manipulation.

Extant REM studies utilize the methodology developed by Roychowdhury (2006) to

calculate abnormal discretionary expenses and identify firms engaging in REM. However, Cohen

et al. (2014) find that this Traditional REM measure is severely mis-specified. Specifically, they

find that the Traditional REM measure indicates the presence of REM far too often in randomly

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constructed samples. Similarly, analysis of the Traditional REM model by Siriviriyakul (2014)

suggests the presence of omitted variables. She states that “[b]ecause real earnings management

is a departure from normal activity, if the REM proxies truly capture REM activity, they should

exhibit subsequent reversal” (Siriviriyakul 2014, 3). Contrary to her expectation, she finds that

the REM proxies are highly persistent, which she interprets as evidence of omitted correlated

variables.

Whereas prior research utilizes cross-sectional analysis to identify firms engaged in real

earnings management, in practice, financial statement analysis is conducted using individual firm

characteristics and fundamentals (e.g., Stickney et al. 2003; White et al. 2003; Lundholm and

Sloan 2009; Penman 2013). In addition, Francis and Smith (2005) argue that earnings analysis

should be performed using firm-specific time-series estimations because the persistence of a

firm’s earnings components are unlikely to depend on the persistence of other firms’ similar

components. Therefore, we develop a firm-specific time-series model that does not rely on

information about other firms within an industry and does not make the assumption that the

persistence of earnings components (e.g., discretionary expenses) are the same for all firms.

Thus, our model provides a method for more accurately forecasting a firm’s expected level of

discretionary expenses, which allows us to identify firms that deviate from those expected levels.

Early empirical evidence of REM focuses on the opportunistic reduction of R&D

expenditures to reduce expenses. Baber, Fairfield and Haggard (1991) find evidence suggesting

that R&D investment decisions are influenced by managers’ concerns about reported earnings,

and Bushee (1998) finds that managers reduce R&D expenditures to meet short-term earnings

benchmarks. Dechow and Sloan (1991) provide evidence that CEOs reduce R&D expenditures

to increase current earnings near the end of their tenure. Finally, Bens, Nagar and Wong (2002)

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provide empirical evidence that firms engage in REM for purposes other than meeting a specific

earnings benchmark. They find that firms experiencing significant employee stock option

exercises shift resources away from real investments, including R&D, toward the repurchase of

their own stock. Consistent with Bushee (1998), they also suggest that this imposes real costs on

the firm.

More recent research finds evidence of REM employing multiple types of real activities

manipulation. Roychowdhury (2006) develops empirical methods to detect these manipulations

in large samples and provides evidence that managers reduce discretionary expenses to avoid

reporting annual losses. Using a modified version of his abnormal discretionary expense

measure, Roychowdhury (2006) also finds less robust evidence that firms use REM to meet

consensus analyst forecasts. Cohen et al. (2008) confirm the findings of Roychowdhury (2006)

and document that, while AM declined after the passage of the Sarbanes-Oxley Act (SOX) in

2002, REM increased significantly. This suggests that firms shifted away from using AM to

REM after SOX. Badertscher (2011) finds that managers engage in earnings management to

support overvalued equity and that they generally exhaust AM strategies before moving to REM.

Finally, Zang (2012) finds that managers use real activities management and accrual-based

earnings management as substitutes and engage in real activities management when its relative

cost is low.

Prior research finds mixed results on the consequences of REM. Cohen and Zarowin

(2010) provide evidence that managers engage in earnings management around seasoned equity

offerings (SEOs) and that declines in post-SEO performance due to REM are more severe than

declines due to AM. Their evidence is important, because it shows that post-SEO

underperformance is driven not just by accruals reversal, but also reflects real consequences of

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operational decisions made to manage earnings. Bhojraj, Hribar, Picconi and McInnis (2009)

show that firms that beat analyst forecasts using both REM and AM have worse future operating

performance and returns, in the subsequent three years, than firms that just miss analyst forecasts

but do not engage in any form of earnings management. Their study, however, does not attempt

to disentangle the effect of AM and REM individually, but rather uses a measure that

incorporates both forms of earnings management. Francis et al. (2011b) find that firms engaging

in REM are more likely to suffer subsequent stock price crashes than firms not engaging in

REM. Additionally, Kim and Park (2013) investigate the impact of REM on auditor’s client-

retention decisions and find that auditor resignations are more likely when firms manage

earnings through opportunistic operating decisions. These results illustrate the negative

consequences and risk associated with REM.

Alternatively, Taylor and Xu (2010) find that REM does not lead to a significant decline

in subsequent operating performance. Similarly, Gunny (2010) provides evidence that firms that

engage in REM to meet or just beat earnings benchmarks have better operating performance, in

the subsequent three years, than firms that either miss the earnings benchmarks or meet the

earnings benchmarks but do not engage in REM. She suggests that engaging in REM is not

opportunistic, but rather a way for managers to produce current-period benefits and to signal

superior future earnings. Zhao et al. (2012) find that although abnormal real activities, in general,

are associated with lower future performance, real earnings management intended to just meet

earnings benchmarks is associated with higher future performance, consistent with real earnings

management conveying a signal of superior future performance.

One possible reason for the inconsistent results in prior research is that different

managers could be engaging in real activities manipulation to achieve different goals, or perhaps

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that the association between REM and future performance varies with the ability levels of

managers. It is possible that behavior that appears to be REM is actually “good” management or

similarly that REM may be used to signal strong future performance. This may be particularly

true for firms with high managerial ability. Demerjian, Lev, and McVay (2012) develop a

managerial ability score that is calculated in two-stages: the first stage provides an estimate of

firm-level operational efficiency and the second stage controls for various firm characteristics to

isolate the effects of the manager.5 Utilizing this managerial ability measure allows us to

determine whether managers with higher ability are better able to mitigate the costs of REM on

future performance and signal stronger future performance, or whether they are perhaps not

engaging in REM at all, but rather engaging in “good” management that leads to better future

performance. Demerjian, Lev, Lewis, and McVay (2013) find that better managers are less likely

to engage in earnings management when it reduces financial reporting quality, and Demerjian,

Lewis-Western, and McVay (2015) find that better managers are able to intentionally smooth

earnings in a way that is low-cost to their firms. These findings suggest that managers may also

be able to engage in REM without negatively impacting future operating performance.

The lack of consensus in prior literature regarding the effects of REM on future operating

performance, and the need for a new methodology of identifying firms suspected of engaging in

REM, motivate this study.

3. Research Design

Managers use earnings management to meet certain earnings benchmarks, achieve

internal bonus targets, appear more attractive prior to initial public offerings (IPOs) and SEOs,

5 The MA-Score data developed by Demerjian et al. (2012) is publicly available for download at

https://community.bus.emory.edu/personal/PDEMERJ/Pages/Download-Data.aspx.

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avoid debt covenant violations, meet regulatory requirements, avoid taxes, and for numerous

other reasons (Graham et al. 2005). Examples of REM include cutting discretionary expenses

such as advertising, R&D, and SG&A, in order to achieve desired outcomes. Theoretically, REM

should be an infrequent event that reverses as firms return to normal levels of activity in

subsequent years. REM, however, is difficult to measure because we cannot observe the

intentions of management. Prior research (e.g., Roychowdhury 2006; Gunny 2010) uses cross-

sectional models to estimate the expected level of discretionary expenses and then compares the

estimate to actual discretionary expenses to determine abnormal discretionary expenses. These

cross-sectional models are estimated by year and industry. Cohen et al. (2014) and Siriviriyakul

(2014) express concern that the traditional models used to measure real earnings management are

severely mis-specified and do not accurately identify firms that depart from normal levels of

expenses. Furthermore, because of the importance of firm-specific characteristics in financial

statement analysis, the analysis is performed at the firm level in practice (e.g., Stickney et al.

2003; White et al. 2003; Lundholm and Sloan 2009; Penman 2013). Consistent with this

argument, Francis and Smith (2005) suggest that earnings analysis should be performed using

firm-specific time-series estimations because the persistence of a firm’s earnings components are

unlikely to depend on the persistence of other firms’ earnings components. To address the

concerns raised in prior literature, we develop a new methodology of measuring REM.

Specifically, we use firm-specific time-series estimations.

3.1 Firm-specific persistence of discretionary expenses

Firms that deviate from their normal levels and report abnormally low levels of

discretionary expenses to meet earnings targets are likely engaging in REM. In our analysis, we

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focus on advertising, R&D, and SG&A expenses, which prior research suggests are commonly

manipulated to meet certain earnings benchmarks. We include advertising and R&D because

they are expensed as incurred and because the future benefit of these expenditures may be

uncertain. As a result, firms may cut advertising and R&D if they feel pressure to hit important

earnings benchmarks in a given year, especially if the benefit from the expenses will not be

realized until future periods. We include SG&A because prior research suggests that SG&A

costs are sticky (Anderson et al. 2003),6 and a significant decrease in SG&A costs in a particular

year is potentially suspicious. Furthermore, SG&A frequently includes costs such as employee

training, maintenance and travel, etc., which are highly discretionary in nature. To identify

abnormal changes in a firm’s discretionary expenses, we sum advertising, R&D, and SG&A

expenses and estimate the following firm-specific regression:

DisExpt = α + β1DisExpt-1 + εt, (1)

where:

DisExpt = the sum of advertising, R&D, and SG&A expenses, scaled by total assets at the

beginning of year t.7

We estimate model (1) for each firm-year using a 10-year rolling window. Using the

estimated values of the intercept and β1 from model (1), we derive the expected level of

discretionary expenses in year t. The abnormal level of discretionary expenses in year t

(AbnDisExpt) is calcualted as the difference between the actual level of discretionary expenses in

year t and the predicted level of discretionary expenses in year t. Because our calcualtion of

abnormal discretionary expenditures is performed at the firm level, our measure addresses the

6 Anderson et al. 2003 suggest that SG&A costs are “sticky” because they decrease less when sales decrease than

they increase when sales increase by an equal amount. This is attributed to both the fixity of certain costs and

managerial decisions to avoid cutting slack resources in anticipation of a rebound in sales. 7 In order to maximize the number of sample observations, and following prior research, we set advertisting expense

and R&D expense to zero if missing.

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concern that some firms are inherently different from other firms in their respective industry. It is

important to note that abnormally low levels of discretionary expenditures are income increasing.

3.2 Identification of suspect firms

Prior research suggests that firms face intense pressure to meet certain earnings

benchmarks, and we expect that firms engage in REM to meet these benchmarks. Following

Roychowdhury (2006), we utilize the following benchmarks to identify firms that are more likely

to be engaging in REM: reporting earnings that meet or just beat zero and meeting or just beating

the consensus anlayst forecast. With respect to the positive earnings benchmark, prior literature

(e.g., Burgstahler and Dichev 1997; Roychowdhury 2006) finds a significant spike in the number

of firms reporting earnings in the interval just to the right of zero (i.e., earnings scaled by lagged

total assets between 0.000 and 0.005) when compared with the interval immediately to the left of

zero (i.e., firms that just missed reporting positive earnings). This suggests that the spike in

earnings just to the right of zero is the result of firms managing earnings to report profits.

Therefore, consistent with prior research, we classify observations that fall into the interval

immediately to the right of zero as suspect firm-years (Bench_NIt).8 Prior research finds that

firms that meet or just beat consensus analyst forecasts suffer severe stock price delcines

(Skinner and Sloan 2002), and these penalties may provide ample incentive for firms to engage

8 Focusing on the interval just to the right of zero presents some potential problems. First, firms that meet or just

beat zero earnings are not likely to be the only firms that engage in real earnings management. Specifically, firms

may use real earnings management to report earnings growth, achieve internal bonus targets, appear more attractive

prior to initial public offerings (IPOs) and secondary equity offerings (SEOs), avoid debt covenant violations, meet

regulatory requirements, avoid taxes, and for numerous other reasons. Second, firms with pre-managed earnings

safely to the right of zero have an incentive to use income-decreasing real earnings management to create reserves,

thus making it easier to achieve earnings targets in the future. This income-decreasing REM could place the firms

just to the right of zero. These problems lower the power of the subsequent empirical tests; however, they are

shortcomings of all earnings management studies that utilize these benchmarks to identify suspect firms.

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in REM. Therefore, consistent with prior research, we classify observations that meet or just beat

consensus analyst forecasts by one cent or less as suspect firm-years (Bench_AFt).

3.3 Model testing the association between REM and meeting earnings benchmarks

To examine whether firms use REM to meet or just beat earnings benchmarks, we

estimate the following model from Gunny (2010):

AbnDisExpt = α + β1Sizet + β2MTBt + β3ROAt + β4Bencht + εt (2)

where:

AbnDisExpt = Firm-Specific REM or Traditional REM;

Firm-Specific_REM = the difference between the actual and expected level of discretionary

expenses in year t derived from model (1);

Traditional_REM = the difference between the actual and expected level of discretionary

expenses derived following Roychowdhury (2006);

Sizet = the natural log of total assets at the beginning of year t;

MTBt = the ratio of the market value of equity to the book value of equity at the

beginning of year t;

ROAt = the difference between the firm-specific income before extraordinary

items in year t, scaled by lagged total assets, and the median income

before extraordinary items in year t, scaled by lagged total assets, for the

same industry (two-digit SIC);

Bencht = Bench_NIt or Bench_AFt.

Bench_NIt = an indicator variable equal to 1 if income before extraordinary items in

year t, scaled by lagged total assets, is between 0 and 0.005, 0 otherwise;

and

Bench_AFt = an indicator variable equal to 1 if the firm meets or just beats the final

consensus analyst forecast before the fiscal year end by one cent or less, 0

otherwise.

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Consistent with Roychowdhury (2006) and Gunny (2010), we include Sizet to control for

size effects and MTBt to control for growth opportunities. Additionally, we include ROAt because

real earnings management may be related to performance. We estimate model (2) in a pooled

regression. All variables are winsorized at the top and bottom 1% of the sample distribution to

mitigate the impact of outliers. Bencht is our variable or interest. We expect a negative

coefficient on Bencht, indicating that firms that report small positive earnings or that meet or just

beat consensus analyst forecasts report abnormally low levels of discretionary expenses, which is

consistent with firms engaging in REM to meet earnings benchmarks.

3.4 Model testing the association between “blip” suspect firms and meeting earnings benchmarks

We perform an additional test to validate that the Firm-Specific REM measure is

capturing real earnings management. We create a “blip” variable for both the Firm-Specific

REM and the Traditional REM measures. The “blip” variable is an indicator variable equal to 1

if abnormal discretionary expenses are negative in year t, but positive in both years t-1 and t+1.

A common criticism of REM studies is that abnormally low discretionary expenses may

represent a change in philosophy by managers to lower expenses moving forward, in order to

produce better future operating performance, or may indicate that some firms are inherently

different from other firms in their industry. This measure is intended to mitigate that criticism by

identifying firms that have abnormally low discretionary expenses in a year that is surrounded by

positive abnormal discretionary expenses. To examine the likelihood that firms with a “blip” are

more likely to meet or just beat zero earnings or zero earnings growth benchmarks, we estimate

the following model:

SuspectMBt = α + β1Blipt +β2Sizet + β3MTBt + β4BigNt + β5ROAt + β6Losst + εt (3)

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where:

SuspectMBt = an indicator variable equal to 1 if income before extraordinary items,

scaled by lagged total assets, is between 0 and 0.005 or if the change in

income before extraordinary items, scaled by lagged total assets, between

years t-1 and t is between 0 and 0.005, 0 otherwise;

Blipt = Firm-Specific_Blipt or Traditional_Blipt;

Firm-SpecificBlipt = an indicator variable equal to 1 if Firm-Specific REM is negative in year

t and positive in both years t-1 and t+1, 0 otherwise;

TraditionalBlipt = an indicator variable equal to 1 if Traditional REM is negative in year t

and positive in both years t-1 and t+1, 0 otherwise;

BigNt = an indicator variable equal to 1 if the firm’s auditor is one of the Big N

audit firms, 0 otherwise;

Losst = an indicator variable equal to 1 if the firm reported negative income

available to common shareholders before extraordinary items in year t, 0

otherwise, and

all other variables as previously defined.

We run the model separately using the Firm-Specific_Blipt measure and then the

Traditional_Blipt measure. A positive coefficient on Blipt would indicate that the likelihood of

meeting earnings benchmarks in year t is greater for firms with an unexpected drop in

discretionary expenses in that year. This test also provides evidence on whether the REM

measures are effectively capturing the underlying real earnings management construct.

3.5 Model testing the association between REM and future performance

Prior theoretical research suggests that REM is value-destroying and imposes real costs

on firms (Ewert and Wagenhofer 2005). Furthermore, Jensen (2005) argues that when firms

make non-value maximizing decisions, firm value is destroyed. However, empirical findings are

mixed on the impact of REM on future operating performance. To examine the impact of real

18

earnings management on future operating performance we follow Gunny (2010) and estimate the

following model:

ROAt+1 or CFOt+1 = α + β1Beatt +β2JustMisst + β3Bench_NIt + β4Neg_AbnDisExpt or

Low_AbnDisExpt + β5Bench_NI*(Neg_AbnDisExpt or Low_AbnDisExpt ) + β6ROAt or

CFOt + β7Sizet + β8MTBt + β9Returnt + β10ZScoret-1+ εt, (4)

where:

ROAt+1 = the difference between the firm-specific income before extraordinary

items in year t+1, scaled by lagged total assets, and the median income

before extraordinary items in year t+1, scaled by lagged total assets, for

the same industry (two-digit SIC);

CFOt+1 = the difference between the firm-specific cash flow from operations in

year t+1, scaled by lagged total assets, and the median cash flow from

operations in year t+1, scaled by lagged total assets, for the same industry

(two-digit SIC);

Beatt = an indicator variable equal to 1 if income before extraordinary items,

scaled by lagged total assets, is greater than 0.005, 0 otherwise;

JustMisst = an indicator variable equal to 1 if income before extraordinary items,

scaled by lagged total assets, is greater than or equal to -0.005 and less

than 0.000, 0 otherwise;

Bench_NIt = an indicator variable equal to 1 if income before extraordinary items in

year t, scaled by lagged total assets, is between 0 and 0.005, 0 otherwise;

Neg_AbnDisExpt = an indicator variable equal to 1 if AbnDisExpt is negative, 0 otherwise;

Low_AbnDisExpt = an indicator variable equal to 1 if AbnDisExpt is in the lowest quintile

ranked by industry and year, 0 otherwise;

CFOt = the difference between the firm-specific cash flow from operations in

year t, scaled by lagged total assets, and the median cash flow from

operations in year t, scaled by lagged total assets, for the same industry

(two-digit SIC);

Returnt = the buy and hold return of the firm over the 12 months of year t minus

the buy and hold return of a portfolio of firms within the same CRSP

decile during year t; and

ZScoret-1 = a measure of bankruptcy risk calculated as 3.3*(pretax income/lagged

total assets) + (sales/lagged total assets) + 1.25*(retained earnings/lagged

19

total assets) + 1.2*((current assets – current liabilities)/lagged total assets),

and

all other variables as previously defined.

We include ROAt to control for firm performance and Sizet to control size. We include

MTBt to control for growth opportunities. We include Returnt to control for the association

between stock return performance and future earnings, and we include ZScoret to control for the

financial health of the firm. We estimate model (4) in a pooled regression. We include industry

and year fixed effects to control for time and industry effects in our sample. In addition, we

control for potential time-series correlation by clustering standard errors by firm and year. All

variables are winsorized at the top and bottom 1% of the sample distribution to mitigate the

impact of outliers. Our coefficient of interest is β5, the interaction between Bencht and

AbnDisExpt. Prior research suggests that real earnings management is potentially value-

destroying because firms sacrifice potential value creating activities at the expense of meeting

earnings benchmarks. However, an alternate view exists. Gunny (2010) finds that firms that use

real earnings management to meet earnings benchmarks have better future performance. She

suggests that firms engage in real earnings management in order to attain benefits that allow the

firm to perform better in the future and to signal stronger future performance. Additionally,

Graham et al. (2005, 35) report that more than half of the CFOs they surveyed would delay a

project even if it entailed a “...small sacrifice in value.” This suggests that, although managers

admit to manipulating real activities to meet earnings benchmarks, they are cognizant of the

potential costs and attempt to keep these costs as low as possible. In light of the two competing

arguments provided by prior research, coupled with the survey evidence of Graham et al. (2005),

we do not make a directional prediction on β5 and expect to find that using REM to meet

earnings benchmarks has neither a positive nor negative impact on future operating performance.

20

Prior research also suggests that high ability managers are able to manipulate income,

through intentional smoothing, in a way that imposes a very low cost on their firms (Demerjian

et al. 2015). Additionally, Demerjian et al. (2013) find that high ability managers are less likely

to engage in earnings management when it reduces financial reporting quality. Therefore, we use

the managerial ability measure developed in Demerjian et al. (2012) to examine whether high

ability managers who engage in REM are able to mitigate the potential negative impacts on

future operating performance. We modify Gunny (2010) and estimate the following model:

ROAt+1 or CFOt+1 = α + β1Firm-Specific_REMt +β2HAMt + β3Firm-Specific_REMt*HAMt +

β4ROAt or CFOt + β5Size + β6MTBt + β7Returnt + β8ZScoret-1+ εt,

(5)

where:

HAMt = an indicator variable equal to 1 when the managerial ability score is

above the median for the industry (two-digit SIC) and year, 0 otherwise,

and

all other variables as previously defined.

Our coefficient of interest in this model is β3, the interaction between Firm-

Specific_REMt and HAMt. If high ability managers are able to engage in REM without negatively

impacting future performance, or if they are not engaging in REM at all, but rather prudently

cutting costs as “good” managers, then we expect the coefficient on β3 to be positive.

3.6 Sample Selection

To examine the relation between REM and future operating performance, we identify all

firm-year observations from the Compustat database between 1995 and 2013. Our sample

consists of firms with available data from the Center for Research in Security Prices (CRSP),

firms where the sum of advertising, R&D, and SG&A expenses is non-zero, and firms with

21

enough data to calculate abnormal discretionary expenses using our firm-specific methodology.

We develop our measure of high managerial ability from publicly available managerial ability

score data. We eliminate firms in regulated industries (SIC codes 4400 to 5000) and banks and

financial institutions (SIC codes 6000 to 7000). These restrictions result in a preliminary sample

of 31,782 firm-year observations (representing 4,383 unique firms). We also winsorize all

variables at the top and bottom 1% of the distribution to eliminate the impact of extreme

observations. We perform the multivariate analyses that follow using the maximum number of

observations with complete data available for each test. Because of this, the number of

observations varies across specifications.

4. Results

4.1 Descriptive Statistics

We report sample descriptive statistics in Table 1. The sample mean (median) of Firm-

Specific_REMt is -0.0077 (-0.0042) with a standard deviation of 0.2039, indicating a wide

variance of abnormal discretionary expenses among the sample firms. The sample mean

(median) of Traditional_REM is 0.0288 ((0.0063) with a standard deviation of (0.2008). The

mean (median) of the natural log of total assets (Sizet) is 5.6910 (5.8020) and the mean (median)

of the market to book ratio is 2.2215 (1.6854). The mean (median) of firm industry-adjusted

return on assets (ROAt) is 0.2193 (0.2030), and the mean (median) of firm industry adjusted cash

flow from operations (CFOt) is 0.1242 (0.1122). Approximately 2% of our firm-year

observations report income before extraordinary items, scaled by lagged total assets, between 0

and 0.005 (Bench_NIt), and approximately 12% meet or just beat consensus analyst forecasts by

one cent or less (Bench_AFt). Approximately 6% of our firm-year observations report income

22

before extraordinary items, scaled by lagged total assets, or the change in income before

extraordinary items, scaled by lagged total assets, between 0 and 0.005 (SuspectMBt).

Approximately 6% of our firm-year observations have negative Firm-Specific REM in year t and

positive Firm-Specific REM in years t-1 and t+1 (FirmSpecific_Blipt), and approximately 2%

have negative Traditional REM in year t and positive Traditional REM in years t-1 and t+1

(Traditional_Blipt). Approximately 56% of our firm-year observations are during years where

the firm was audited by a Big N auditor (BigNt), and approximately 30% are years with negative

income (Losst). The mean (median) of one-year ahead return on assets (ROAt+1) is 0.2291

(0.2141) and the mean (median) of one-year ahead cash flow from operations (CFOt+1) is 0.1277

(0.1148)., Approximately 68% of our firm-year observations have income before extraordinary

items, scaled by lagged total assets, greater than 0.005 (Beatt), and approximately 1% have

income before extraordinary items, scaled by lagged total assets, between -0.005 and 0 of lagged

assets (JustMisst). Approximately 56% of our firm-year observations have negative abnormal

discretionary expenses (Neg_AbnDisExpt), and approximately 18% have abnormal discretionary

expenses ranked in the lowest quitile their industry (Low_AbnDisExpt). The mean (median) 12

month buy and hold return (Returnt) is -0.0331 (-0.0465). The mean (median) Z Score (ZScoret)

is 1.0783 (1.9341). Finally, approximately 42% of our firm-year observations are under the

direction of high ability managers.

[Insert Table 1 here]

In Table 2, we present Pearson correlations for select variables of our sample

observations. We find that the correlations between our Firm-Specific measure of abnormal

discretionary expenses (Firm-Specific_REMt) is negatively and significantly associated with

Sizet, MTBt, ROAt, Firm-Specific_Blipt, Traditional_Blipt, Beatt, Neg_AbnDisExpt, and

23

Low_AbnDisExpt, and is positively and significantly associated with HAMt. We find that the

correlations between the Traditional measure of abnormal discretionary expenses

(Traditional_REMt) is negatively and significantly associated with ROAt, Bench_NIt,

Suspect_MBt, Firm-Specific_Blipt, Traditional_Blipt, Beatt, JustMisst, Neg_AbnDisExpt, and

HAMt, and is positively and significantly associated with Sizet, MTBt, Bench_AFt, and

Low_AbnDisExpt.

[Insert Table 2 here]

In Figure 1, we examine and compare the persistence of the Firm-Specific REM measure

with the persistence of the Traditional REM measure. Siriviriyakul (2014) contends that the

Traditional REM measure is highly persistent and mis-specified, which is an indication of

correlated ommitted variables. If a firm engages in REM to achieve certain earnings benchmarks,

we would expect the firm to exhibit abnormally low levels of abnormal discretionary expenses in

the manipulation years and then a return to higher levels of abnormal discretionary expenses as

the expenses are shifted, or return to normal levels, in prior and subsequent periods. We present

the percentage of surviving firms that have abnormal discretionary expenses with the same sign

(either positive or negative) in all years prior, for both the Traditional REM measure and the

Firm-Specific REM measure. We also compare the measures to the likelihood that abnormal

discretionary expenses would exhibit the same sign (either positive or negative) under the

assumption of random chance. The figure illustrates that the Traditional REM measure is highly

persistent, a criticism echoed by Siriviriyakul (2014). In fact, using the traditional methodology,

13 out of the 21 firms (or 61.9%) that survive the entire eighteen year sample period have the

same abnormal discretionary expenses sign. In other words, using the traditional methodology,

nearly two-thirds of the surviving firms have either eighteen consecutive years of positive

24

abnormal discretionary expenses or eighteen consecutive years of negative abnormal

discretionary expenses. By comparison, none of the sample firms have the same sign of

abnormal discretionary expenses for the full eighteen year period using our firm-specific

methodology. This is compared to the 0.00038% chance that a firm would have the same sign of

abnormal discretionary expenses for the full eighteen years under the assumption of random

chance. Thus the Firm-Specific REM measure appears to alleviate the concerns expressed by

Siriviriyakul (2014).

[Insert Figure 1 here]

4.2 Empirical Results

In Table 3 we present results from estimating model (2), where we examine whether

firms that meet or just beat zero earnings or consensus analyst forecast benchmarks report lower

levels of abnormal discretionary expenses. In Columns (1) and (2), we report results where

Bencht is equal to Bench_NIt, and the dependent variable in Column (1) (Column (2)) is Firm-

Specific_REM (Traditional_REM). Consistent with our expectations, in Column (1) we find that

the coefficient on Bench_NIt is negative and significant, at the 5% level, suggesting that firms

use real earnings management to avoid reporting negative earnings. In Column (2), we also find

that the coefficient on Bench_NIt is negative and significant, at the 10% level, confirming the

findings from Roychowdhury (2006) that firms use real earnings management to avoid reporting

negative earnings, albeit at a lower level of statistical significance. In Columns (3) and (4), we

report results where Bencht is equal to Bench_AFt, and the dependent variable Column (3)

(Column (4)) is Firm-Specific_REM (Traditional_REM). Consistent with our expectations, we

find that the coefficient on Bench_AFt in Column (1) is negative and significant, at the 1% level,

25

suggesting that firms use real earnings management to meet or just beat consensus analyst

forecasts. However, in Column (4), we do not find a statistically significant coefficient on

Bench_AFt, suggesting either that firms do not use real earnings management to meet or just beat

consensus analyst forecasts or, alternatively, that the Traditional REM measure is not accurately

identifying abnormal discretionary expenses, and, therefore, is less accurately identifying real

earnings management.

[Insert Table 3 here]

In Table 4, we provide supplemental analysis on the likelihood that firms are using real

earnings management to meet or just beat zero earnings or zero earnings change benchmarks and

whether the Traditional REM measure and the Firm-Specific REM measure are effective at

capturing the construct of real earnings management under an alternate specification. In Column

(1), Firm-Specific_Blipt is the variable of interest and in Column (2), Traditional_Blipt is the

variable of interest. These “blip” variables are indicators equal to one for firms with negative

abnormal discretionary expenses in year t and positive abnormal expenses in years t-1 and t+1,

and 0 otherwise. These measures are intended to capture whether firms that have abnormally

lower discretionary expenses in year t, surrounded by years with positive abnormal discretionary

expenses are, on average, cutting discretionary expenses to meet or just beat zero earnings or

zero earnings change benchmarks. In Column (1) we find that the coefficient on Firm-

Specific_Blipt is positive and significant at the 1% level, suggesting that these “blip” firms are

engaging in REM to meet or just beat these earnings benchmarks. In Column (2) we find that the

coefficient on Traditional_Blipt is not statistically significant, suggesting either that “blip” firms

do not use REM to meet these earnings benchmarks or, alternatively, that the Traditional REM

measure is less accurately identifying REM.

26

[Insert Table 4 here]

In Table 5, we present results from estimating model (4), where we examine the impact

of real earnings management on future firm performance. In Columns (1) and (2), the dependent

variable is ROAt+1 and in Columns (3) and (4), the dependent variable is CFOt+1. In Columns (1)

and (3) we use an indicator variable for negative abnormal discretionary expenses,

Neg_AbnDisExpt. In Columns (2) and (4) we rank AbnDisExpt into quintiles and construct

Low_AbnDisExpt, which is an indicator variable equal to one if AbnDisExpt is in the lowest

quintile, and zero otherwise. In Column (2) we find that the coefficient on Low_AbnDisExpt is

negative and significant, indicating that low abnormal discretionary expenses are negatively

associated with future return on assets. However, we do not find statistically significant

coefficients on the interaction between Bench_NIt and either Neg_AbnDisExpt or

Low_AbnDisExpt, suggesting that firms that report lower abnormal discretionary expenses in

order to meet earnings benchmarks perform neither better nor worse in the future than firms

without abnormally low discretionary expenses. This result supports the survey evidence from

Graham et al. (2005), and our prediction, that managers only use REM to meet earnings

benchmarks when the negative impact on firm value is small.

[Insert Table 5 here]

In Table 6, we present results from estimating model (5), where we examine the impact

of high ability managers on the association between real earnings management and future firm

performance. In Column (1), the dependent variable is ROAt+1 and in Column (2), the dependent

variable is CFOt+1. Our variable of interest is the interaction between Firm-Specific_REMt and

HAMt, which is an indicator variable equal to one if managerial ability is above the median for

the industry and year, and zero otherwise. In Column (1) we find that the coefficient on Firm-

27

Specific_REMt*HAMt is positive and significant, indicating that high ability managers that

engage in REM produce higher ROA in future periods. However, we do not find a statistically

significant coefficient on Firm-Specific_REMt*HAMt in Column (2), suggesting that high ability

managers that engage in REM produce neither higher nor lower CFO in future periods. This

result supports the evidence found in Demerjian et al. (2015) that high ability managers are able

to engage in earnings management (intentional income smoothing) at a low cost to the firm.

5. Conclusion

REM refers to opportunistic operational decisions made by management to influence

reported earnings. Prior research measures REM using cross-sectional analysis, which is used to

identify firms suspected of engaging in REM and to investigate its impact on future firm

performance (e.g., Roychowdhury 2006; Cohen et al. 2008; Cohen and Zarowin 2010; Gunny

2010). Recent papers, however, argue that the traditional measures of REM are severely mis-

specified (Cohen et al. 2014; Siriviriyakul 2014). In response to these papers, and in light of

conflicting evidence on the impact of REM on future performance, we develop a firm-specific

time-series measure of REM.

A firm-specific time-series measure is ideal because it eliminates the concern that the

REM measure is merely capturing changes or differences in management philosophy or inherent

firm differences. We first address concerns that the traditional measure of REM is mis-specified

by providing evidence that our firm-specific REM measure is less persistent than the traditional

REM measure. Second, we find that income-increasing REM, using both the firm-specific and

the time-series measures, is associated with meeting or just beating zero earnings. Importantly,

however, we find that only income-increasing REM is associated with meeting or just beating

28

the consensus analyst forecast only when using the firm-specific measure. In our final analyses,

we examine the impact of REM on future firm performance, the results of which are mixed in

prior research. Using the firm-specific measure, we do not find evidence that firms that

opportunistically engage in REM perform either better or worse in the future. This result is

consistent with survey responses from Graham et al. (2005), who suggest that managers are

careful not to sacrifice future operating performance in order to meet earnings benchmarks. In

additional analyses, we find that high ability managers that engage in REM produce higher

ROAs in the subsequent year, apparently mitigating the hypothesized negative impacts that REM

has on future operating performance and accurately signaling stronger future performance. Our

results should be of interest to investors and researchers as they consider the measurement and

implications of real earnings management.

29

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33

FIGURE 1

Percentage of Observations that have Abnormal Discretionary Expenses with the Same

Sign as All Years Prior

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10 t+11 t+12 t+13 t+14 t+15 t+16 t+17

Percentage of Surviving Firms that have Abnormal Discretionary Expenses with the Same Sign in all Years

Traditional Measure Firm-Specific Measure Random Chance

34

TABLE 1

Descriptive Statistics

Variable N Mean Std. Dev.

25th

Percentile Median

75th

Percentile

Firm-Specific_REMt 31,782 -0.0077 0.2039 -0.0358 -0.0042 0.0182

Traditional_REM 31,782 0.0288 0.2008 -0.0792 0.0063 0.1272

Sizet 31,782 5.6910 2.3173 4.0013 5.8020 7.4143

MTBt 31,782 2.2215 2.6443 0.9772 1.6854 2.8400

ROAt 31,693 0.2193 0.2471 0.0680 0.2030 0.3560

CFOt 31,693 0.1242 0.1707 0.0264 0.1122 0.2139

Bench_NIt 31,782 0.0188 0.1361 0.0000 0.0000 0.0000

Bench_AFt 15,039 0.1190 0.3237 0.0000 0.0000 0.0000

Suspect_MBt 31,782 0.0636 0.2440 0.0000 0.0000 0.0000

Firm-Specific_Blipt 31,782 0.0564 0.2307 0.0000 0.0000 0.0000

Traditional_Blipt 31,782 0.0177 0.1318 0.0000 0.0000 0.0000

BigNt 31,782 0.5584 0.4966 0.0000 1.0000 1.0000

Losst 31,782 0.3012 0.4588 0.0000 0.0000 1.0000

ROAt+1 27,893 0.2291 0.2500 0.0731 0.2141 0.3677

CFOt+1 27,893 0.1277 0.1720 0.0285 0.1148 0.2171

Beatt 31,782 0.6799 0.4665 0.0000 1.0000 1.0000

Just_Misst 31,782 0.0137 0.1163 0.0000 0.0000 0.0000

Neg_AbnDisExpt 31,782 0.5646 0.4958 0.0000 1.0000 1.0000

Low_AbnDisExpt 31,782 0.1816 0.3855 0.0000 0.0000 0.0000

Returnt 22,093 -0.0331 0.5452 -0.3379 -0.0465 0.2382

ZScoret 31,201 1.0783 4.2031 0.8638 1.9341 2.8565

HAMt 31,782 0.4215 0.4938 0.0000 0.0000 1.0000

This table presents descriptive statistics for our sample firms from 1995 through 2013. All variables are

winsorized at the 1st and 99th percentiles. Variable definitions are provided below:

Firm-Specific_REM = the difference between the actual and expected level of discretionary expenses

in year t derived from model (1);

Traditional_REM = the actual level of discretionary expenses in year t minus the expected level of

discretionary expenses in year t derived following Roychowdhury (2006);

Sizet = the natural log of total assets at the beginning of year t;

MTBt = the ratio of the market value of equity to the book value of equity at the

beginning of year t;

ROAt = the difference between the firm-specific income before extraordinary items in

year t, scaled by lagged total assets, and the median income before extraordinary

items in year t, scaled by lagged total assets, for the same industry (two-digit

SIC);

35

CFOt = the difference between the firm-specific cash flow from operations in year t,

scaled by lagged total assets, and the median cash flow from operations in year t,

scaled by lagged total assets, for the same industry (two-digit SIC);

Bench_NIt = an indicator variable equal to 1 if NIt is between 0 and 0.005 of lagged

total assets, 0 otherwise;

Bench_AFt = an indicator variable equal to 1 if the firm meets or just beats the final

consensus analyst forecast before the fiscal year end by one cent or less, 0

otherwise;

Suspect_MBt = an indicator variable equal to 1if income before extraordinary items, scaled by

lagged total assets, is between 0 and 0.005 or if the change in income before

extraordinary items, scaled by lagged total assets, between years t-1 and t is

between 0 and 0.005, 0 otherwise;

Firm-Specific_Blipt = an indicator variable equal to 1 if Firm-Specific REM is negative in year t and

positive in both years t-1 and t+1, 0 otherwise;

Traditional_Blipt = an indicator variable equal to 1 if Traditional REM is negative in year t and

positive in both years t-1 and t+1, 0 otherwise;

BigNt = an indicator variable equal to 1 if the firm’s auditor is one of the Big N audit

firms, 0 otherwise;

Losst = an indicator variable equal to 1 if the firm reported negative income available

to common shareholders before extraordinary items during the year t, 0

otherwise;

ROAt+1 = the difference between the firm-specific income before extraordinary items in

year t+1, scaled by lagged total assets, and the median income before

extraordinary items in year t+1, scaled by lagged total assets, for the same

industry (two-digit SIC);

CFOt+1 = the difference between the firm-specific cash flow from operations in year t+1,

scaled by lagged total assets, and the median cash flow from operations in year

t+1, scaled by lagged total assets, for the same industry (two-digit SIC);

Beatt = an indicator variable equal to 1 if net income is greater than 0.005 of lagged

total assets, 0 otherwise;

Just_Misst = an indicator variable equal to 1 if income before extraordinary items, scaled by

lagged total assets, is greater than or equal to -0.005 and less than 0.000, 0

otherwise;

Neg_AbnDisExpt = an indicator variable equal to 1 if AbnDisExpt is negative, 0 otherwise;

Low_AbnDisExpt = an indicator variable equal to 1 if AbnDisExpt is in the lowest quintile ranked

by industry and year, 0 otherwise;

Returnt = the buy and hold return of the firm over the 12 months of year t minus the buy

and hold return of a portfolio of firms within the same CRSP decile during year t;

and

ZScoret = a measure of bankruptcy risk calculated as 3.3*(pretax income/lagged total

assets) + (sales/lagged total assets) + 1.25*(retained earnings/lagged total assets)

+ 1.2*((current assets – current liabilities)/lagged total assets);

HAMt = an indicator variable equal to 1 when the managerial ability score is

above the median for the industry (two-digit SIC) and year, 0 otherwise.

36

TABLE 2

Pearson Correlations

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Firm-Specific_REM (1)

Traditional_REM (2) 0.068

Size (3) -0.038 0.015

MTB (4) -0.014 0.130 0.087

ROA (5) -0.341 -0.079 0.184 0.095

Bench_NI (6) -0.008 -0.015 0.011 -0.021 -0.008

Bench_AF (7) -0.012 0.019 0.025 0.077 0.068 -0.023

Suspect_MB (8) -0.003 -0.027 0.090 -0.003 0.016 0.532 0.010

Firm-Specific_Blip (9) -0.041 -0.029 -0.009 -0.013 -0.005 0.005 -0.006 0.016

Traditional_Blip (10) -0.031 -0.048 -0.016 -0.006 -0.017 0.009 -0.015 -0.005 0.067

Beat (11) -0.025 -0.083 0.317 0.097 0.309 -0.201 0.086 -0.023 -0.023 -0.025

Just_Miss (11) -0.007 -0.017 0.015 -0.027 0.001 -0.016 -0.015 -0.013 0.008 0.005 -0.172

Neg_AbnDisExp (13) -0.338 -0.093 0.052 -0.012 0.058 0.012 -0.002 0.005 0.215 0.054 0.003 0.008

Low_AbnDisExp (14) -0.356 0.080 -0.124 0.017 -0.035 0.001 0.013 -0.033 0.014 0.078 -0.082 -0.006 0.413

HAM 0.016 -0.014 0.011 0.055 0.047 -0.036 0.042 -0.014 0.001 -0.014 0.200 -0.034 -0.024 0.004

This table presets Pearson correlations between select variables for the entire sample of 31,782 firm-year observations over the period

1995-2013. Variable definitions are provided in Table 1.

37

TABLE 3

The Association between Suspect Firms and Abnormal Discretionary Expenses

Dependent Variable:

Firm-

Specific

REM

Traditional

REM

Firm-

Specific

REM

Traditional

REM

(1) (2) (3) (4)

Intercept ? -0.0130*** -0.0347*** -0.0177*** -0.0927***

(<.0001) (<.0001) (<.0001) (<.0001)

Sizet ? 0.0004** 0.0026*** 0.0006** -0.0149***

(0.0394) (<.0001) (0.0268) (<.0001)

MTBt ? 0.0004* -0.0238*** 0.0008*** 0.0251***

(0.0956) (<.0001) (0.0036) (<.0001)

ROAt ? -0.0154*** -0.2642*** 0.0135*** -0.2608***

(<.0001) (<.0001) (0.0087) (<.0001)

Bench_NIt - -0.0051** -0.0108*

(0.0284) (0.0579)

Bench_AFt - -0.0037*** 0.0046

(0.0044) (0.1130)

N 31,782 31,782 15,039 15,039

Adjusted R2 0.0007 0.0705 0.0018 0.0847

This table presents robust regression estimation results for Model (2) in which the dependent variable is

Abnormal Discretionary Expense (AbnDisExpt) using either the Firm-Specific or the Traditional

methodology. For Columns (1) and (2), the variable of interest is Bench_NIt, and for Columns (3) and (4),

the variable of interest is Bench_AFt. P-values are reported below the coefficient estimates and are based

on t-statistics clustered by firm. All variables are winsorized at the 1st and 99th percentiles. Variable

definitions are provided in Table 1. ***, **, and * represent significance at the 1%, 5%, and 10% levels,

respectively, using a two-tailed t-test, except for on Bench_NIt and Bench_AFt, where a directional

prediction is made, and one-tailed t-tests are used.

38

TABLE 4

The Association between Blip Suspect Firms and Meeting or Just Beating Zero Earnings or

Last Year’s Earnings

Firm-Specific Traditional

(1) (2)

Intercept ? -2.4910*** -2.4712***

(<.0001) (<.0001)

Firm-Specific_Blipit ? 0.3082***

(0.0008)

Traditional_Blipit ? -0.0464

(0.8026)

Sizeit ? 0.0358*** 0.0359***

(0.0013) (0.0012)

MTBit ? -0.0125* -0.0128*

(0.0746) (0.0690)

BigNit ? 0.1314*** 0.1301***

(0.0079) (0.0086)

ROAit ? -0.3145*** -0.3136***

(<.0001) (<.0001)

Lossit ? -1.9686*** -1.9642***

(<.0001) (<.0001)

N 31,728 31,728

Adjusted R2 0.0194 0.0190

This table presents robust regression estimation results for Model (3) in which the dependent variable is

Blipt, using either the Firm-Specific or the Traditional methodology. For Column (1), the variable of

interest is Firm-Specific_Blipt, and for Column (2), the variable of interest is Traditional_Blipt. P-values

are reported below the coefficient estimates and are based on t-statistics clustered by firm. All variables

are winsorized at the 1st and 99th percentiles. Variable definitions are provided in Table 1. ***, **, and *

represent significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test.

39

TABLE 5

The Association between Abnormal Discretionary Expenses and Future Performance

Dependent Variable: ROAt+1 CFOt+1

(1) (2) (3) (4)

Intercept - -0.0283** -0.0277** -0.0545*** -0.0545***

(0.0208) (0.0227) (<.0001) (<.0001)

Beatt + 0.0181** 0.0178** 0.0270*** 0.0269***

(0.0123) (0.0133) (<.00001) (<.0001)

Just_Misst ? -0.0075 -0.0079 0.0112 0.0112

(0.5179) (0.4928) (0.2029) (0.2033)

Bench_NIt ? -0.0238* -0.0129 0.0194** 0.0171***

(0.0655) (0.1695) (0.0216) (0.0019)

Neg_AbnDisExpt ? -0.0032 0.0017

(0.2235) (0.2578)

Bench_NIt* Neg_AbnDisExpt ? 0.0204 -0.0056

(0.1348) (0.5762)

Low_AbnDisExpt ? -0.0074*** -0.0015

(0.0095) (0.4588)

Bench_NIt*Low_AbnDisExpt ? 0.0071 -0.0063

(0.6435) (0.5731)

ROAt + 0.5024*** 0.5022***

(<.0001) (<.0001)

CFOt + 0.5249*** 0.5247***

(<.0001) (<.0001)

Sizet ? 0.0071*** 0.0069*** 0.0052*** 0.0052***

(<.0001) (<.0001) (<.0001) (<.0001)

MTBt + 0.0027*** 0.0027*** 0.0020*** 0.0020***

(<.0001) (<.0001) (<.0001) (<.0001)

Returnt + 0.0411*** 0.0410*** 0.0184*** 0.0183***

(<.0001) (<.0001) (<.0001) (<.0001)

ZScoret-1 + 0.0112*** 0.0112*** 0.0062*** 0.0062***

(<.0001) (<.0001) (<.0001) (<.0001)

Industry FE Included Included Included Included

Year FE Included Included Included Included

N 21,026 21,026 21,026 21,026

Adjusted R2 0.6237 0.6237 0.6372 0.6372

40

This table presents robust regression estimation results for Model (4) in which the dependent variable is

ROAt+1 in Columns (1) and (2) and CFOt+1 in Columns (3) and (4). In Columns (1) and (3) the variable of

interest is Bench_NIt*Neg_AbnDisExpt and in Columns (2) and (4) the variable of interest is

Bench_NIt*Low_AbnDisExpt. P-values are reported below the coefficient estimates and are based on t-

statistics clustered by firm. All variables are winsorized at the 1st and 99th percentiles. Variable definitions

are provided in Table 1. Industry fixed-effects (two digit SIC code) and year fixed-effects are included in

the model but are not reported. Variable definitions are provided in Table 1. ***, **, and * represent

significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test, except where a

directional prediction is made, and a one-tailed t-test is used.

41

TABLE 6

The Impact of High Ability Managers on the Association between Abnormal Discretionary

Expenses and Future Performance

Dependent Variable: ROAt+1 CFOt+1

(1) (2)

Intercept ? -0.0140*** -0.0419***

(0.0002) (<.0001)

Firm-Specific_REMt ? -0.0171 -0.0222

(0.3786) (0.1075)

HAMt ? 0.0092*** 0.0530***

(<.0001) (<.0001)

Firm-Specific_REMt*HAMt ? 0.0685*** -0.0229

(0.0044) (0.6281)

ROAt + 0.5430***

(<.0001)

CFOt + 0.5130***

(<.0001)

Sizet ? 0.0059*** 0.0053***

(<.0001) (<.0001)

MTBt + 0.0035*** 0.0030***

(<.0001) (<.0001)

Returnt + 0.0458*** 0.0205***

(<.0001) (<.0001)

ZScoret-1 + 0.0072*** 0.0050***

(<.0001) (<.0001)

Industry FE Included Included

Year FE Included Included

N 21,053 21,026

Adjusted R2 0.6939 0.6375

This table presents robust regression estimation results for Model (5) in which the dependent variable is

ROAt+1 in Columns (1) and (2) and CFOt+1 in Columns (3) and (4). In Columns (1) and (3) the variable of

interest is Firm-Specific_REMt*HAMt. P-values are reported below the coefficient estimates and are based

on t-statistics clustered by firm. All variables are winsorized at the 1st and 99th percentiles. Variable

definitions are provided in Table 1. Industry fixed-effects (two digit SIC code) and year fixed-effects are

included in the model but are not reported. Variable definitions are provided in Table 1. ***, **, and *

represent significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test, except where

a directional prediction is made, and a one-tailed t-test is used.